Controlling Inference Using the Query Tree

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Citation: Alon Y. Halevy and Yehoshua Sagiv. (1993) Controlling Inference Using the Query Tree. In KSL-93-07, January,1993.

Publication techreport ( Edit )
type Technical Report
bibtype techreport
Bibtex basics
author Alon Y. Halevy and Yehoshua Sagiv
title Controlling Inference Using the Query Tree
number KSL-93-07
institution Knowledge Systems, AI Laboratory
year 1993
month January
Bibtex more
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abstract Controlling inference is a key to scaling up AI systems. This paper describes methods for controlling inference in Horn rule knowledge bases using a powerful tool, the {\em query tree}. The query tree is a finite structure that encodes all the possible derivations of a query. It shows which facts in the knowledge base may be used in a derivation of the query. Furthermore, it encodes all the possible sequences of rule applications and database lookups that can result in answers to the query. Consequently, it can be used to control search both by ignoring certain facts and by guiding the search of the problem solver to pursue only useful paths. The distinguishing characteristic of the query tree is that under certain conditions, it encodes {\em only} useful derivation paths and tells us {\em precisely} which facts can be used in a derivation of the query. We present experimental results showing that in practice, using the query tree to control search leads to significant savings.

KSL Technical Report ID: KSL-93-07
Facts about Controlling Inference Using the Query TreeRDF feed
Abstract Controlling inference is a key to scaling … Controlling inference is a key to scaling up AI systems. This paper describes methods for controlling inference in Horn rule knowledge bases using a powerful tool, the {\em query tree}. The query tree is a finite structure that encodes all the possible derivations of a query. It shows which facts in the knowledge base may be used in a derivation of the query. Furthermore, it encodes all the possible sequences of rule applications and database lookups that can result in answers to the query. Consequently, it can be used to control search both by ignoring certain facts and by guiding the search of the problem solver to pursue only useful paths. The distinguishing characteristic of the query tree is that under certain conditions, it encodes {\em only} useful derivation paths and tells us {\em precisely} which facts can be used in a derivation of the query. We present experimental results showing that in practice, using the query tree to control search leads to significant savings. ntrol search leads to significant savings.
Author Alon Y. Halevy and Yehoshua Sagiv  +
Bibtype techreport  +
Has author Alon Y. Halevy and Yehoshua Sagiv  +
Has identifier KSL-93-07  +
Has publishing details January,1993  +
Has title Controlling Inference Using the Query Tree  +
Has where published KSL-93-07  +
Has year 1993  +
Institution Knowledge Systems, AI Laboratory  +
Ksl tr id KSL-93-07  +
Month January  +
Number KSL-93-07  +
Process note NO  +
Title Controlling Inference Using the Query Tree  +
Year 1993  +
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